AI Applyd

Optimize Resume with AI

aiapplyd_optimize_resume

Rewrite a resume to pass ATS screening. Optionally tailor it to a job description, then get the rewritten resume, a projected ATS score, and an itemized list of every change made.

Instructions

Rewrite a resume so it passes ATS screening. Returns the rewritten resume, a projected ATS score, and an itemised summary of every change made. Pass job_description to tailor the rewrite to one specific posting, or omit it for general ATS optimization. Requires a connected AI Applyd account. Uses the user's AI credits. Do not use it to apply: aiapplyd_apply tailors the resume for every application on its own.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`resume_text`	Yes	Full text of the resume to optimize
`job_description`	No	Full text of the target job description (optional, omit for general ATS optimization)

Output Schema

TableJSON Schema

Name	Required	Description	Default
`changes`	Yes
`optimizedResume`	Yes
`projectedAtsScore`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedv1.8.0Sep 25, 2026

removedInput schema / additionalProperties
```
Removed value: -false
```

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": false,
+  "properties": {
+    "changes": {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "optimizedResume": {
+      "type": "string"
+    },
+    "projectedAtsScore": {
+      "anyOf": [
+        {
+          "type": "number"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    }
+  },
+  "required": [
+    "optimizedResume",
+    "changes",
+    "projectedAtsScore"
+  ],
+  "type": "object"
+}

First observedv1.3.0Sep 7, 2026

TDQS

A4.7/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds meaningful behavioral context beyond annotations: it discloses that the tool uses the user's AI credits and requires a connected account. It also states that it returns a rewritten resume and a summary, implying it does not modify the stored resume directly. The annotations already indicate it is not read-only and not idempotent, so the description complements them with concrete side effects (credit usage) and prerequisites. It could go further by clarifying whether the rewrite is saved or only returned, but the current level is strong.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded: it opens with the primary purpose and outputs, then covers optional usage, prerequisites, and an explicit sibling differentiation. Every sentence serves a purpose with no redundancy. It is well-structured and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (mutation, credit consumption, optional tailoring), the description covers all essential aspects: what it does, what it returns, when to use the optional parameter, prerequisites, and when not to use it. The output schema exists, so return values are partially documented, but the description still summarizes them clearly. No critical information is missing for an agent to invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already describes both parameters fully (100% coverage), so the baseline is 3. The description adds value by explaining the purpose of job_description ('to tailor the rewrite to one specific posting') and the omit condition ('general ATS optimization'), which is not explicit in the schema. It also implies that resume_text is the full text to be optimized, aligning with the schema. This goes beyond a simple restatement of parameter names.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a clear verb+resource: 'rewrite a resume so it passes ATS screening.' It also lists the exact outputs (rewritten resume, ATS score, itemised summary) and distinguishes itself from the sibling aiapplyd_apply by explicitly warning not to use it for applying. This makes the tool's purpose unambiguous and sets it apart from other resume-related siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives explicit usage guidance: pass job_description to tailor to one posting, or omit for general optimization. It also names aiapplyd_apply as the alternative for applying and states 'Do not use it to apply', leaving no ambiguity about when to choose this tool over a sibling. Additionally, it mentions prerequisites (connected AI Applyd account) and credit consumption, which are relevant usage constraints.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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